AI

Artificial intelligence can help patients interpret home tests for COVID-19

Mary Cunningham — Fri, 27 Jan 2023 18:59:27 +0000

Artificial intelligence can help patients interpret home tests for COVID-19 Mary Cunningham Fri, 01/27/2023 - 13:59

In This Story

Researchers led by Professors Farrokh Alemi and Janusz Wojtusiak found that computerized symptom screenings can supplement at-home COVID-19 tests to better confirm the diagnosis for patients and clinicians.

New machine learning research led by Professor Farrokh Alemi and Professor Janusz Wojtusiak provides a way for patients and clinicians to better predict whether symptoms are due to COVID-19, influenza, or RSV. A more accurate diagnosis leads to better decisions on course of care to heal patients and prevent the disease from spreading. With fellow George Mason University researchers and Vibrent Health, Alemi and Wojtusiak recently published a series of articles in a special edition of the Journal of Quality Management in Healthcare discussing how artificial intelligence (AI) can help in the diagnosis of COVID from a combination of symptoms and home tests.

With their research, Alemi and Wojtusiak are now working on a website to deliver an AI-based resource to assist individuals in identifying recommended actions as a result of their clinical profile and COVID at-home test results.

“We see AI working to radically improve clinical triage and test-to-treat decisions,” said Wojtusiak. Alemi added, “AI will allow individuals to feel more confident about their decisions to stay home, seek care, or to socially isolate. Lots of people test at end of their symptoms and surprisingly they find they are still positive. What does one do if symptoms and home test results do not agree? Our AI will help these individuals understand how to proceed.”

The study in paper 1 (as listed below) found that the timing of symptoms matters in a COVID diagnosis. For example, a runny nose as an early symptom increased the odds of testing positive for COVID, and a runny nose as a symptom that occurred later decreased the odds. Similarly, fever is almost always a late symptom, so lack of fever early on should not be used to rule out COVID.

The results in paper 2 found that COVID cannot be diagnosed from individual symptoms; however, a cluster of three or more symptoms can aid in diagnosis. Findings from paper 4 found the accuracy of diagnosing COVID symptoms was highest when symptoms from different body symptoms were present. For example, a combination of neurological and common respiratory symptoms was more diagnostic than either one of the sets of symptoms individually. In addition, COVID has different presentations depending on age, severity of illness, and virus mutations.

Paper 3 discusses how an AI symptom screening could improve, and for vaccinated individuals replace, at-home antigen tests. At-home tests are not always accurate and require clinical review, but these tests are done at home where no such review is available. AI symptom screening can help make these tests more accurate. The study reports that AI symptom screening is more accurate than taking a second home test.

The four papers published in the special supplement are:

A fifth paper, titled Modeling the Probability of COVID-19 Based on Symptom Screening and Prevalence of Influenza and Influenza-Like Illnesses, from same group of researchers was also published in the Journal of Quality Management in Healthcare in April/June 2022.

Alemi was Mason’s principal investigator. Mason was a subcontractor to Vibrent Health, where Praduman Jain was the principal investigator of the project. (Jain is a member of Mason’s College of Public Health advisory board.) Other Mason-affiliated researchers on these projects include Associate Professor Amira Roess, affiliate faculty member Jee Vang, doctoral student Elina Guralnik, former student and adjunct faculty Wejdan Bagais. Rachele Peterson and Josh Schilling from Vibrent Health and F. Gerard Moeller from Virginia Commonwealth University were also part of the research team.

The research was funded by the program called “Digital Health Solutions for COVID-19” launched by the National Cancer Institute (NCI) and the National Institute of Biomedical Imaging and Bioengineering (NIBIB).

The methods used in these five papers vary. In paper 4, researchers conducted a meta-analysis of the literature, using data from published papers. In the other papers, researchers surveyed patients who took a PCR test and examined the relationship between the patients’ symptoms and PCR test results. Most research was done using data collected between October 2020 and January 2021, prior to the current variants such as BA.5 or BQ.1.

Previous, related publications by these investigators include a study examining how computers can distinguish between COVID-19 and flu and an analysis of symptomatic university students and social distancing.

Topics

coronavirus; covid-19

Covid-19

COVID-19; Research

Artificial Intelligence

Jinshan Tang

Emma Therese Anderson — Fri, 10 Sep 2021 20:12:24 +0000

Jinshan Tang Emma Therese A… Fri, 09/10/2021 - 16:12

Professor, HAP

Contact Information

Email: jtang25@gmu.edu

Phone: 703-993-3697

CV

Download Jinshan Tang curriculum vitae (CV) here.

Personal Websites

ORCID

Personal website

Biography

Dr. Jinshan Tang is a professor of Health Informatics in the Department of Health Administration and Policy. Before joining George Mason University, Dr. Tang was a full professor in the College of Computing at Michigan Technological University and a Founding Director of the Joint Center for Biocomputing and Digital Health. Dr. Tang’s research covers broad areas related to image processing and artificial intelligence. His specific research interests include biomedical image analysis, biomedical imaging, artificial intelligence in medicine (e.g., computer-aided cancer detection, AI for COVID-19 detection). He has obtained over three million dollars grants as a PI or Co-PI and has published more than 130+ refereed journal and conference papers. He has also served as a committee member at various international conferences. He is a senior member of IEEE and a Co-chair of the Technical Committee on Information Assurance and Intelligent Multimedia-Mobile Communications, IEEE SMC society.

Research

Research Interests

Medical Image Analysis
Artificial Intelligence in healthcare
Biomedical Imaging
Computer Aided Cancer Detection
AI for COVID-19 Detection and Diagnosis
Big Data Analysis
Bioinformatics

Publications

Juanjuan He, Qi Zhu*, Kai Zhang, Piaoyao Yu, Jinshan Tang, "An evolvable adversarial network with gradient penalty for COVID-19 infection segmentation", Volume 113, Part B,107947, Applied Soft Computing, December 2021.
Chen Zhao*, Aviral Vij, Saurabh Malhotra, Jinshan Tang, Haipeng Tang, Drew Pienta, Zhihui Xu, Weihua Zhou, “Automatic extraction and stenosis evaluation of coronary arteries in invasive coronary angiograms”, vol. 136, 104667, Computers in Biology and medicine, September 2021.
Nan Mu, Hongyu Wang*, Yu Zhang*, Heng Yang*, Jinshan Tang, “Progressive Global Perception and Local Polishing Network for Lung Infection Segmentation of COVID-19 CT Images”, Pattern Recognition, 120:108168, Dec,2021
Xiaoming Liu, Aihui Yu*, Xiangkai Wei*, Zhifang Pan*, Jinshan Tang, “Multimodal MR Image Synthesis Using Gradient Prior and Adversarial Learning,” IEEE Journal of Selected Topics in Signal Processing, vol. 14, no.6, pp. 1176 – 1188, Oct. 2020.
X Liu, Jinshan Tang, “Mass Classification in Mammograms Using Selected Geometry and Texture Features and a New SVM-Based Feature Selection Method”, IEEE System Journal, pp. 910~920, vol. 8 No.3, 2014.
Jinshan Tang, Steven Millington, Scott Acton, Jeff Crandall, and Shepard Hurwitz, “Surface Extraction and Thickness measurement of the Articular Cartilage from MR images using Directional Gradient Vector Flow Snake,” IEEE Tr. On Biomedical Engineering, Vol. 52, No. 5, 2006.

Awards

The Best Paper Award, SPIE Conference on Mobile Multimedia/Image Processing, Security, and Applications, Baltimore, MD, USA, May 21, 2015.
Achievement Award, The 2001 International Multi-conference, Las Vegas, Nevada, USA, June 25-28, 2001
Keynote speaker, the 9th International Conference on Frontiers of Intelligent Computing: Theory and Applications, National Institute of Technology Mizoram, India, June 25 - 26, 2021.
Keynote speaker, International Conference on Urban Intelligence and Applications, Wuhan, China, May 10-12, 2019.
Keynote speaker, at the 14th National Symposium on Sustainable Development of Medical Information Education and the 5th National Medical Information Graduate Forum of the Chinese Medical Association Medical Information Branch, Zunyi, Guizhou, China, July 25, 2019.
Invited talk, SPIE Conference on Intelligent Robots and Computer Vision XVIII: Algorithms, Techniques, and Active Vision, Boston, 1999.
Top 10% of teachers for student evaluation in MTU (Spring Semester 2014)
Postdoctoral Fellowship, National Institutes of Health 2004-2005
Postdoctoral Fellowship, Harvard Medical School, 2000-2001

Professional Affiliations/Memberships

Co-Chair, Technical committee of Information Assurance & Intelligent Multimedia- Mobile Communications of the IEEE Systems, Man, and Cybernetics Society (2016~)
Senior Member, Institute of Electrical and Electronics Engineers (IEEE) (2003~)

Degrees

Post-doctoral training, National Cancer Institute, National Institute of Health
Post-doctoral training, Schepens Eye Research Institute and Department of Ophthalmology, Harvard Medical School.
Ph.D., Telecommunication and electronic system, Beijing University of Posts and Telecommunications,1998
MS, Control theory and applications, Heilongjiang University, 1995
BS, Math education, Xiangtan Normal University, 1992